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Ismail and Al-Abudi Iraqi Journal of Science, 2016, Special Issue, Part B, pp: 428-440

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Email:Moh 1972 [email protected] *

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Satellite image classification using KL-transformation and modified vector

quantization

Rafah Rasheed Ismail *, Bushra Q. Al-Abudi

Department of Astronomy and Space, College of Science, University of Baghdad, Baghdad , Iraq.

Abstract

In this work, satellite images classification for Al Chabaish marshes and the area

surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two

software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented.

Proposed supervised classification method (Modified Vector Quantization) using

MATLAB software and supervised classification method (Maximum likelihood

Classifier) using ERDAS imagine have been used, in order to get most accurate

results and compare these methods. The changes that taken place in year 2000

comparing with 1990 and in year 2015 comparing with 2000 are calculated. The

results from classification indicated that water and vegetation are decreased, while

barren land, alluvial soil and shallow water are increased for year 2000 comparing

with 1990. Water, vegetation and barren land are increased, while alluvial soil and

shallow water decreased for years 2015 comparing with 2000. The classification

accuracy for the proposed method (MVQ) is 90.1%, 90.9% and 90.2% for years

1990, 2000 and 2015, respectively.

Keywords: Satellite images, classification, and Modified Vector

Quantization (MVQ).

( Modifide Vector Quntization)و (KLتصنيف صور االقمار الصناعية باستخدام تحويل )

بشرى قاسم العبودي , *رفاه رشيد اسماعيل , بغداد , العراق. قسم الفمك والفضاء , كمية العموم , جامعة بغداد

الخالصةلجبايش والمنطقة المحيطة بها في تم في هذا العمل تصنيف صور القمر الصناعي الندسات الهوار ا

ERDAS 2014 (MATLABباستخدام برنامجين ) 0102و0111و0991محافظة ذي قار لمسنوات Modified Vector. تم تطبيق طريقتين من طرق التصنيف المشرف عميها وهي الطريقة المقترحة ),7.11

Quantization )باستخدام برنامج MATLAB 7.11 وطريقة ( (Maximum likelihood Classifierبرنامج باستخدامERDAS imagine 2014 ذلك لمحصول عمى أدق النتائج ومن ثم مقارنة و

. وكذلك 0991بالمقارنة مع سنة 0111نتائج هذه الطريقتين و حساب التغييرات التي حدثت في سنة ج أن المناطق المائية ومناطق . أشارت النتائ0111بالمقارنة مع سنة 0102التغييرات التي حدثت في سنة

النباتات في حالة تناقص بينما مناطق المياة الضحمة والتربة الطينية واالراضي القاحمة في حالة زيادة في عام بينما أشارت النتائج أن المناطق المائية ومناطق النباتات واالرض القاحمة 0991بالمقارنة مع عام 0111

ISSN: 0067-2904

Ismail and Al-Abudi Iraqi Journal of Science, 2016, Special Issue, Part B , pp:428-440

429

بالمقارنة مع عام 0102المياة الضحمة والتربة الطينية في حالة تناقص في عام في حالة تزايد بينما مناطق

91.0% و91.9% و91.0( كانت MVQ. ان دقة التصنيف لطريقة التصنيف الموجهة المقترحة )0111 . عمى التوالي 0102و 0111و0991لمسنوات

1. Introduction

Remote sensing is the acquisition of physical data of an object, area or phenomenon without touch

or contact with the target under investigation using various sensors to detecting and recording

electromagnetic radiation from the target areas in the field of view of the sensors

instruments[1].Satellites remote sensing is equipped with sensors looking down to the earth. Since the

early 1960s, numerous satellite sensors have been launched into orbit to observe and monitor the Earth

and its environment. First Landsat satellite was launched in July 1972. Landsat satellites (series 1- 8)

providing repetitive, synoptic, global coverage of high resolution multispectral imagery, they have

potential applications for monitoring the conditions of the Earth's surface and the environment

components [2].

Digital image classification is the process of assigning pixels to classes. These classes form regions on

a map or an image, so that after classification the digital image is presented as a mosaic of uniform

parcels, each identified by a color or symbol. Image classification is an important part of the fields of

remote sensing, image analysis, and pattern recognition. The two general approaches which are used

most often are: supervised and unsupervised classification [1].

In this paper, we applied Korhunen- Loeve (KL) transformation on six bands of satellite image for

Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and

2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) to create newly

integrated image with dense information and best contrast due to the information of all used bands are

concentrated in one integral image, two methods of classification have been used to classify area of

these newly images; these were supervised proposed method Modified Vector Quantization (MVQ)

using (MATLAB 7.11) and supervised methods (Maximum likelihood Classifier),using (ERDAS

imagine 2014) to get the most accurate results and then detect environmental changes in the study area

for the period 1990,2000 and 2015.This results are compatible with our field view which is done in (1

May 2016).

2. The Study Area

Al-Chibaish Marshes comprised a vast complex of mostly permanent freshwater marshes, with

scattered areas of open water, located to the west of the River Tigris and north of the River Euphrates

(within Basra, Thi-Qar, and Missan provinces). Covering an area of (3000 km2), it is bounded by the

longitudes 46° 47ʹ to 47° 21ʹ E and Latitude 30° 55ʹ to 31° 23ʹ N and represent many diverse habitats

from seasonal to permanent marshes. Figure-1, shows the original map of Iraq and the image inside

the polygon represents study area. While Figure-2 , show the study area for period 1990, 2000 and

2015 respectively. The satellite image for the study area is capture from landsat-7TM, lansat-7TM+

which are chosen bands are (1, 2,3,4,5 and 7) for years 1990 and 2000 and landsat-8OLI which are

chosen bands are (2,3,4,5,6 and 7) for year 2015, With 30 m spatial resolution.


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Figure 1- Location of the study area on the map of Iraq

Figure 2- The study area (Al-Chibaish Marshes and the area surrounding) for years 1990, 2000 and

2015


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3. Change Detection

Remote sensing techniques give quick methods to detect the environmental changes, such that

change detection. Change detection is a process of identifying differences in the state of objects or

phenomena by observing them at different time (multi-temporal analysis), therefore change detection

became useful tool for detecting land cover changes. The process of change detection is premised on

the ability to measure temporal effects. It has enabled to observe changes over large areas and

provided long-term monitoring capabilities. In general digital change detection techniques using

temporal remote sensing data are useful to help analyzed these data, and provided detailed information

for detecting change in land cover [3]. Image algebra is a widely used change detection technique that

involved one of two methods; band subtraction or the band ratio. The subtraction method involves

subtracting a digital number value from one date of a specified band from the digital number value of

the same pixel in the later date. The subtraction results in positive and negative values where change

has occurred. This method gives the information for compute the total area that has changed [4].

4. Image Transformation

Two-dimensional image transforms are very important in image processing. The image produce in

the transformed may be analyzed, explicate, and further processed for investigation diverse image

processing tasks. These transformations are exceedingly used, since by using these transformations, it

is conceivable to express an image as a combination of a set of basic signals, known as the basis

functions. Such transforms, reveal spectral structures embedded in the image that may be used to

characterize the image [5]. Image Transformations are performed using Operators. An Operator takes

as input an image and produces another image; produces image is created from linear set of pixels of

an input image. Linear transforms have been utilized to enhance images and to extract various features

from images [6].

5. KL- Transformation

The principle objective of transformation techniques is to process the use of different imagery

bands lead to get different details in the images; this enabled us to collect greater details to be

represented in one image using KL transforms. This transform uses the input image bands to create

new other principles components (PCs) bands. The resulted image will have characteristics of dense

information and best contrast. The first PC is suitable for the purpose of classifying the multiband

satellite image, where the other remaining PCs are ignored. The KL transform was applied using

(MATLAB 7.11) on six bands (1, 2, 3, 4, 5, 7) of Al-Chabaish Marshes satellite images for years

(1990,2000 ) and six bands (2, 3, 4, 5,6, 7) for year 2015 as shown in Figure-3.

Figure3- The six bands of Al-Chabaish Marshes satellite image for years 1990, 2000 and 2015

.

Six new bands carried the principal components (PC) as shown in Figure-4, for years 1990, 2000,

2015. Displays the six PC images resulted from the KL transform; it is shown that just the first PC

image is integrated well since it shows the fine details clearly in comparison with the input ones

presented in Figure-3, respectively or the other PC images. This makes the (Principal Component 1) of


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three Figure-4, are suitable for the purpose of classifying the multiband satellite image, whereas the

other remaining PCs are ignored.

Figure 4- The Six Output Images after using KL-Transform of Al-Chabaish Marshes satellite image

for years 1990, 2000, and 2015.

6. The Proposed supervised Classification Method (Modified Vector Quantization). VQ is a technique for approximating a set of input vectors into a finite number of a codebook is

generated for each image. Image classification using vector quantization is a fast search routine, which

is used to divides the given image into number of groups, and computes the centroids of the clusters

(codebook). The method most commonly used to generate codebook is the (VQ) algorithm which is

also called as Linde-Buzo-Gray (LBG) algorithm [7]. In this work we proposed Modified Vector

Quantization to perform supervised classification method. The following steps illustrate the

mechanism of this proposed classification method:-

Read the satellite image.

1. For an M M image, the image is first partitioned into fixed size square blocks, each block of

size n n. In this work we applied block size (44)

2. Form an initial codebook by choosing the first N-input image vectors as reproduction vectors.

In this work we selected first five blocks of image, each of these initial codebook represent one of the

classes (water, vegetation, alluvial soil, shallow water and Barren land).

3. Calculate the centroid of all image's codebook.

4. Create specified threshold (ei). In this work (ei = 0.01).

5. Add and subtract specified threshold from the codebook and generate two vector v1 and v2.

6. Compare each input vector with all N-reproduction vectors. Best match is achieved when the

minimum mean square error (MSE) between the reproduction and the input vectors is within a pre-

specified threshold. In this case the input matched vectors should be given the same index of the

reproduction vector.

7. The procedure repeats until the process converges to solution, which is a minimum of the total

reproduction error.

8. For each index, find the centroid of all input vectors. The centroids are the new codebook.

9. Sort the codebook vectors in descending order from high count to low count.

10. Reconstruct an image from the quantized vectors.

11. The classification decision is made according to number of codebook, the codebook assigned

to the class.

The algorithms discussed above are implemented using (MATLAB 7.11). The results from

applying (MVQ) classification method and The Percentage of each class can be shown in Figures (5-

7) for years 1990, 2000 and 2015, respectively. Table-1, presents the change in the years 2000, 2015

comparing with 1990 and the change in the year 2000 comparing with 2015, Figure-8, shown change

in this period. The results indicated that five classes found with comparison with the original image.


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These classes represent five major features in the study area (water, vegetation, alluvial soil, shallow

water and Barren land).

Figure 5: supervised classification using modified Vector quantization and the Percentage of each

class for Landsat image of Al-Chabaish Marshes in year (1990).

Figure 6- supervised classification using modified Vector quantization and the Percentage of each


Figure 7- supervised classification using modified Vector quantization and the Percentage of each



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Table 1- The change in the years 2000, 2015 comparing with 1990 and the change in the year 2000

comparing with 2015 using modified vector quantization

Classes

Area

( Pixel)

1990

Area

( Pixel )

2000

Area

( Pixel )

2015

Changes

between

1990 and

2000

Changes

between

1990 and

2015

Changes

Between

2000 and

2015

Barren

land 118914 272126 359083

153212

128.84%

240169

201.969%

86957

31.95%

Alluvial soil 195359 321450 137068

126091

64.543%

-58291

-29.838%

-184382

-57.359%

Vegetation

396256 99489 198562

-296767

-74.893%

-197694

-49.891%

99073

99.582%

Water

71742 14724 121623

-57018

-79.477%

49881

69.528%

106899

726.019%

Shallow

water 266305 340787 232240

74482

27.969%

-34065

-12.792%

-108547

-31.852%

Figure 8-The change in 1990, 2000 and 2015using modified Vector quantization.

7. Supervised Classification Method (Maximum Likelihood Classification) Maximum likelihood classification is the most common supervised classification method used with

remote sensing image data. It is a statistical decision criterion to assist in the classification of

overlapping signatures; pixels are assigned to the class of the highest probability. After achieving the

statistical characterization for each information class, the classification made based on the reflectance

of each pixel image, and making a decision about which of the signatures resembles most. The

maximum likelihood classifier gave more accurate results than Spectral Angle Mapper; however, it is

much slower due to extra computations. The final discriminate function g (x) is taken as.

…… (1)

Where: i = class, x = n-dimensional data (where n is the number of bands), p(ωi) = probability that

class ωi occurs in the image and is assumed the same for all classes, |Σi| = determinant of the

covariance matrix of the data in class ωi, Σi-1 = its inverse matrix, mi = mean vector.

In order to reduce poor classification due to small probabilities, threshold values Ti are determined

for each class based on that 95% of the pixels would be classified. According to x2 tables, the

threshold values Ti can be obtained by:

………………… (2)

Finally, we can get the decision rule for maximum likelihood supervised algorithm:

Classes don’t meet the above decision rule will be classified as unknown class [8].


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Supervised classification method (Maximum likelihood Classification) are applied by using

ERDAS imagine 2014. The results and the percentage of each class can be shown in Figures (9-11) for

years 1990, 2000 and 2015, respectively. Table-2, presents the change in the years 2000, 2015

comparing with 1990 and the change in the year 2000 comparing with 2015, Figure-12, shown change

in this period. The results indicated that five classes found with comparison with the original image.

These classes represent five major features in the study area (water, vegetation, alluvial soil, shallow

water and Barren land).

Figure 9- supervised classification using(Maximum Likelihood classifier) and the Percentage of each

class in year (1990).

.

Figure 10- supervised classification using (Maximum Likelihood classifier) and the Percentage of

each class in year (2000).


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Figure 11-supervised classification using (Maximum Likelihood classifier) and the Percentage of each

class in year (2015)

Table 2-The change in the years 2000, 2015 comparing with 1990 and the change in the year 2000

comparing with 2015 using Maximum Likelihood classifier method

Changes

Between

2000 and

2015

Changes

between

1990 and

2015

Changes

between

1990 and

2000

Area

(hectare)

2015

Area

(hectare)

2000

Area

(hectare)

1990

Classes

164454

25.491%

543813

204.596%

379359

142.724% 809612 645158 265799

Barren

land

-319576

-26.761%

6365

0.7331%

325941

37.539% 874634 1194210 868269

Alluvial

soil

420357

172.726%

-504870

-43.203%

-925227

-79.175% 663723 243366 1168593

Vegetatio

n

202344

468.975%

-94410

-27.776%

-296754

-87.306% 245490 43146 339900

Water

-479745

-44.037%

36936

6.449%

516681

90.212% 609675 1089420 572739

Shallow

water

Figure 21- The change in 1990, 2000 and 2445 using (Maximum Likelihood classifier method)


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8. Field Work

Field work Includes checking the Land Cover classes, checking the environmental changes that

took place in these classes. The field survey of this study was done in (1 May 2015). The data were

collected from different sites depending on the nature of the various Land Cover classes prevailing in

the area and sites different soil types in the study area. The geographic coordinates of each site was

checked and registered using Global Positioning System (GPS) device to allow integration image

classification system and also to check any variation in a position of features identified on the ground

compared with the Landsat images. The field data was useful for images analyst to have a better

understanding, explain, and interpret the land cover change and also the relationships between the

satellite images and actual ground conditions. Classes have been noticed such as (water, vegetation,

alluvial soil, shallow water and Barren land). Figure-13, shows some photos which taken in the field

work.

Figure 13- photographs scenes during the visiting to the studied area.

9. Data accuracy

In this work, to estimate the accuracy of classification, we used the information collected during the

field view and number of random points which chosen area for different Structure of Bands to the


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study area which are represented as a classification map to be compared with the classification results

by means the parameters, these parameters represent the total accuracy, user's accuracy, and producer

accuracy respectively, which can be represented mathematically by the following empirical relations:

then compared with the classification results. The classification accuracy assessment was done for

proposed supervised classification methods for years 1990, 2000 and 2015. The most common error

estimates are overall accuracy, User's accuracy and Producer's accuracy. Where overall accuracy is

obtained by dividing the sum of the main diagonal entries of the confusion matrix by the total number

of samples, User's accuracy is the ratio between the number of correctly classified and the row total

because users are concerned about what percentage of the classes has been correctly classified and

Producer's accuracy is the ratio between the number of correctly classified and the column total [9].

Tables (3 - 5) present error matrix with producers, users, and overall accuracy calculations for

supervised proposed classification method (MVQ) for years 1990, 2000 and 2015, respectively.

Table 3- Error matrix with producers, users, and overall accuracy calculations for supervised

classification method (MVQ) for year (1990).

user's

accuracy

Row

total

Vegetation

Water

Shallow

water

Alluvial

soil

Barren

land Classes

87.88% 33 0 0 1 3 29 Barren land

86.67% 30 0 0 2 26 2 Alluvial soil

82.86% 35 2 3 29 1 0 Shallow

water

94.74% 38 0 36 2 0 0 Water

96.296% 54 52 2 0 0 0 Vegetation

190 54 41 34 30 31 Column total

96.296% 87.805% 85.29% 86.67% 93.55% Producer’s

Accuracy

90.526% Total

accuracy



user's

accuracy

Row

total

Vegetation

Water

Shallow

water

Alluvial

soil

Barren

land Classes

90% 40 0 0 1 3 36 Barren land

93.48% 46 0 0 1 43 2 Alluvial soil

80.77% 26 2 2 21 1 0 Shallow

water

92% 25 0 23 2 0 0 Water

92.86% 28 26 2 0 0 0 Vegetation

165 28 27 25 47 38 Column total

92.86% 85.19% 84% 91.49 94.74% Producer’s

Accuracy

90.303% Total

accuracy


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user's

accuracy

Row

total

Vegetation

Water

Shallow

water

Alluvial

soil

Barren

land Classes

90.91% 44 0 0 0 4 40 Barren land

86.84% 38 1 0 2 33 2 Alluvial soil

90.39% 52 1 1 47 3 0 Shallow

water

93.55% 31 0 29 2 0 0 Water

90.74% 54 49 2 3 0 0 Vegetation

219 51 32 54 40 42 Column total

96.08% 90.63% 87.04% 82.5% 95.2% Producer’s

Accuracy

90.411% Total

accuracy

10. Conclusions

The basic idea of our work is to classify Landsat satellite images at different times for the study

area using two supervised methods and then compare the results of each method. From the obtained

results we get the following conclusions:

1. The results from applying Modified Vector Quantization (MVQ) classification method show that

marsh vegetation and water decreased about 74.893%, 79.477%, respectively. While Barren land,

alluvial soil and Shallow water increase about 128.844%, 64.543% and 27.969%, respectively in

2000 compares with 1990, after drying the marshes. While the results after inundation marshes

show that marsh vegetation, water and Barren land increase about 99.582%, 726.019% and

31.95% respectively. While alluvial soil and Shallow water decreased about 57.359% and

31.852% respectively in 2015 compares with 2000.

2. The results from applying supervised (Maximum likelihood) Classification method show that

marsh vegetation and water decreased about 79.175%, 87.306%, respectively. While Barren land,

alluvial soil and Shallow water increase about 142.724%, 37.539% and 90.212%, respectively in

2000 compares with 1990, after drying the marshes. While the results after inundation marshes

show that marsh vegetation, water and Barren land increase about 172.726%, 468.975% and

25.491% respectively. While alluvial soil and Shallow water decreased about 26.761% and

44.037% respectively in 2015 compares with 2000.

3. The classification accuracy for the proposed method (MVQ) is 90.526 %, 90.303% and 90.411%

for years 1990, 2000, 2015 respectively.

4. There are clear environmental changes in the Iraqi marshes during the period 1990- 2015, the

results indicate a decrease in vegetation and water with the increase in the barren land and alluvial

soils and shallow water.

5. The results are agreement with our field view to the study area in (1 May 2016).

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